New energy heat pump integrated control method and system

Abstract

The invention relates to the technical field of heat pump control, in particular to a new energy heat pump integrated control method and system, and the method comprises the steps: dynamically adjusting the supply intensity of new energy based on a real-time collection result, and obtaining an initial energy supply scheme; constructing a load prediction model, predicting thermal load demands at different time periods, and dynamically adjusting operation parameters of the heat pump; according to the initial function scheme and the change trend of the thermal load demand, balance is carried out on supply and demand fluctuation under the condition of surplus and insufficient energy; performing dynamic analysis on the environment data and the equipment operation state which are acquired in real time based on an optimization algorithm; and generating a dynamic energy supply control strategy according to the initial energy supply scheme, the load prediction result, the supply and demand fluctuation balance strategy and the optimized operation parameters. According to the method, the problems of unstable new energy supply, dynamic load demand change and unbalanced supply and demand are effectively solved, the system operation efficiency, the energy supply stability and the energy utilization rate are improved through a dynamic energy supply control strategy, and the method adapts to the requirements of multiple working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of heat pump control, and in particular to a new energy heat pump integrated control method and system. Background Art

[0002] Existing new energy heat pump technology uses renewable energy such as solar energy and geothermal energy to power the heat pump system. However, due to the instability and volatility of new energy supply, it is easy to cause an imbalance between supply and demand, especially when solar radiation is insufficient or the ambient temperature changes greatly. In addition, the dynamic changes in user heat load demand and the lack of efficient energy storage and management mechanisms make it difficult for existing systems to achieve stable control of multi-condition operation and efficient energy utilization. Fixed parameter adjustment or simple load forecasting methods are often used in existing technologies, which are difficult to cope with real-time control needs in complex environments, resulting in low system operation efficiency, energy waste and discontinuous energy supply. Summary of the invention

[0003] The present invention provides a new energy heat pump integrated control method and system, thereby effectively solving the problems pointed out in the background technology.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: A new energy heat pump integrated control method, comprising: Based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, the supply intensity of new energy is dynamically adjusted to obtain the initial energy supply plan; Build a load forecasting model to predict the heat load demand in different periods of time, and dynamically adjust the heat pump operating parameters based on the forecast results; According to the initial functional scheme and the changing trend of heat load demand, balance the fluctuation of supply and demand under the conditions of energy surplus and shortage; Dynamically analyze the real-time collected environmental data and equipment operating status based on optimization algorithms to optimize operating parameters; A dynamic energy supply control strategy is generated based on the initial energy supply plan, load forecasting results, supply and demand fluctuation balancing strategy and optimized operating parameters.

[0005] Furthermore, based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, the supply intensity of new energy is dynamically adjusted to obtain an initial energy supply plan, including: Collecting environmental data as a basis for analyzing the characteristics of new energy supply; Based on the environmental data collected in real time, analyzing the supply characteristics and short-term supply capacity of new energy, and calculating the load demand in combination with the current operation demand of the heat pump; Based on the analysis results of new energy supply and demand, the available new energy input power is calculated and the initial energy supply plan for heat pump operation is generated; The energy supply scheme is dynamically optimized through fuzzy logic algorithms to balance the fluctuation of new energy and the operation of heat pumps.

[0006] Furthermore, based on the real-time collected data, the supply characteristics and short-term supply capacity of new energy are analyzed, and the load demand is calculated in combination with the current operation demand of the heat pump, including: Collecting multi-source data and operating status data related to load demand and standardizing the data as input for load demand calculation; Use a hierarchical forecasting model to calculate load demand and generate dynamic load forecast results; The energy supply area is divided into multiple sub-areas, the load demand of each area is calculated separately, and coordinated adjustments are made between areas to generate the global load demand.

[0007] Furthermore, a hierarchical forecasting model is used to calculate the load demand and generate dynamic load forecasting results, including: Analyze real-time environmental data based on long short-term memory networks to predict load demand in the next few hours and obtain short-term prediction results; Analyze historical load data and seasonal fluctuation trends based on deep neural networks to obtain long-term trend forecast results; The short-term prediction result and the long-term trend prediction result are fused through weighted calculation to generate the dynamic load prediction result.

[0008] Furthermore, a load forecasting model is constructed to predict the heat load demand in different periods of time, and the heat pump operating parameters are dynamically adjusted according to the forecast results, including: According to the dynamic load forecasting results, the target load value of each time period is calculated, and the high load demand time period is marked as a priority processing time period; Based on the target load value, the heat pump operating parameters are dynamically adjusted, and the operating parameters are optimized and adjusted through continuous monitoring of the heat pump system operating status and real-time environmental changes.

[0009] Furthermore, the optimization algorithm is a particle swarm optimization algorithm.

[0010] Furthermore, the particle swarm optimization method comprises: Randomly generate a particle population, each particle represents a combination of heat pump operating parameters, and set the inertia weight and learning factor; Construct objective functions based on minimizing energy consumption, maximizing load matching, and improving operational stability, and calculate particle fitness values; According to the fitness value of the particle, the individual optimal value of the particle and the global optimal value of the population are updated, and the operating parameters of the heat pump are adjusted by updating the speed and position; The global optimal solution is gradually approached in multiple iterations until the preset convergence conditions are met and the optimal operating parameters are obtained.

[0011] Furthermore, the analytic hierarchy process is used to integrate the initial energy supply plan, load forecasting results, supply and demand fluctuation balance strategy and optimized operating parameters to generate the dynamic energy supply control strategy.

[0012] A new energy heat pump integrated control system, the system comprising: The initial plan acquisition module dynamically adjusts the supply intensity of new energy based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, to obtain the initial energy supply plan; The prediction model building module builds a load prediction model to predict the heat load demand in different periods of time and dynamically adjusts the heat pump operating parameters based on the prediction results; A supply and demand fluctuation balancing module, which balances the supply and demand fluctuations under the conditions of energy surplus and shortage according to the initial functional scheme and the changing trend of the heat load demand; Dynamic analysis and optimization module, which dynamically analyzes the real-time collected environmental data and equipment operating status based on the optimization algorithm to optimize the operating parameters; The energy supply strategy generation module generates a dynamic energy supply control strategy based on the initial energy supply plan, load forecasting results, supply and demand fluctuation balancing strategy and optimized operating parameters.

[0013] Furthermore, the initial solution acquisition module includes: An environmental data collection unit collects environmental data, which is used as a basis for analyzing the characteristics of new energy supply; A supply capacity analysis unit, based on the environmental data collected in real time, analyzes the supply characteristics and short-term supply capacity of new energy, and calculates the load demand in combination with the current operation demand of the heat pump; An input power calculation unit calculates the available input power of new energy according to the analysis results of new energy supply and demand, and generates an initial energy supply plan for the operation of the heat pump; The fuzzy algorithm optimization unit dynamically optimizes the energy supply plan through fuzzy logic algorithm to balance the fluctuation of new energy and the operation of heat pumps.

[0014] The technical solution of the present invention can achieve the following technical effects: It effectively solves the problems of unstable new energy supply, dynamic changes in load demand and imbalance between supply and demand. Through dynamic energy supply control strategies, it improves system operation efficiency, energy supply stability and energy utilization rate to adapt to multiple working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of an integrated control method for a new energy heat pump; Figure 2 A schematic diagram of the process for obtaining the initial energy supply scheme; Figure 3 Schematic diagram of the process for calculating load demand; Figure 4 A schematic diagram of the process for generating dynamic load forecast results; Figure 5 Schematic diagram of the process for dynamically adjusting the operating parameters of the heat pump according to the prediction results. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0019] Embodiment 1 like Figure 1 As shown, the present invention provides a new energy heat pump integrated control method, the method comprising: S1: Based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, dynamically adjust the supply intensity of new energy to obtain the initial energy supply plan; Specifically, based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, the supply intensity of new energy is dynamically adjusted to effectively respond to the volatility and intermittent characteristics of new energy supply. The supply of new energy (such as solar energy and wind energy) is significantly affected by weather, time and environmental conditions, and its supply intensity may change at any time. By dynamically adjusting the supply intensity, energy can be fully utilized when new energy is in surplus, and resources can be reasonably allocated when supply is insufficient, thereby improving the utilization efficiency of new energy. The core goal of this step is to match the actual load demand of the system. The load demand of the heat pump system varies depending on the time period and operating conditions. For example, the heating demand during the day and at night is quite different. By analyzing the current load demand through real-time collected data and adjusting the supply intensity, it can ensure that the energy supply plan not only meets the demand, but also avoids the problem of over-supply or under-supply; in addition, this step is also aimed at balancing the stability of the system operation. The fluctuation of new energy supply may cause the heat pump system to operate unstably, and even cause equipment overload or energy waste. By dynamically adjusting the supply intensity of new energy, the impact of supply and demand fluctuations on system stability can be reduced, providing a basic guarantee for the efficient operation of the entire system.

[0020] S2: Build a load forecasting model to predict the heat load demand in different periods, and dynamically adjust the heat pump operating parameters based on the forecast results; Specifically, the purpose of building a load forecasting model, predicting the heat load demand at different times, and dynamically adjusting the heat pump operating parameters according to the forecast results is to optimize the system operating efficiency, improve the energy supply matching, and provide support for subsequent control strategies. Through accurate prediction, changes in load demand can be grasped in advance, and operating parameters such as compressor frequency and refrigerant flow can be dynamically adjusted to avoid over- or under-energy supply, thereby reducing energy waste and improving the balance between supply and demand; at the same time, load forecasting combined with real-time environmental data and historical trend analysis can help the system adapt to complex working conditions, ensure efficient and stable operation, and provide key inputs for optimization algorithms and dynamic energy supply control.

[0021] S3: Balance supply and demand fluctuations under conditions of energy surplus and shortage based on the initial functional plan and the changing trend of heat load demand; Specifically, according to the initial energy supply plan and the changing trend of heat load demand, the supply and demand fluctuations are balanced under the conditions of energy surplus and shortage, which is mainly achieved by dynamically adjusting the energy supply strategy. When new energy is surplus, the system gives priority to meeting the energy demand of high-load areas to ensure the stable operation of key loads, and at the same time calls energy storage equipment for energy storage, such as charging or heat storage, to avoid energy waste. In addition, by optimizing energy supply by zoning, the energy supply intensity of non-critical load areas is reduced to improve the overall energy utilization efficiency; in the case of insufficient energy, the system gives priority to reducing or suspending the energy supply of non-critical loads, and allocating limited energy resources to key areas. At the same time, the system can call energy storage equipment to release energy to supplement the supply and demand gap, and reduce the total energy consumption by adjusting the operating parameters of the heat pump (such as reducing the compressor frequency, reducing the refrigerant flow, etc.), prolonging the duration of energy supply, and avoiding the system from being unstable due to excessive load. In the whole process, the combination of real-time monitoring and load forecasting results helps the system dynamically adjust the energy supply plan, respond to the changes in load demand during peak and trough periods in advance, and smooth out supply and demand fluctuations.

[0022] S4: Dynamically analyze the real-time collected environmental data and equipment operating status based on the optimization algorithm to optimize the operating parameters; Specifically, the optimization algorithm is used to dynamically analyze the real-time collected environmental data and equipment operating status, optimize the operating parameters of the heat pump system, thereby improving the system's operating efficiency and supply and demand balance capabilities. Through a comprehensive analysis of environmental data (such as temperature, humidity, wind speed, etc.) and equipment operating status (such as compressor frequency, refrigerant flow, etc.), the operating parameters are dynamically adjusted to make the energy supply intensity highly matched with the load demand. This optimization can not only reduce energy waste and minimize energy consumption, but also improve overall energy utilization.

[0023] S5: Generate a dynamic energy supply control strategy based on the initial energy supply plan, load forecast results, supply and demand fluctuation balance strategy and optimized operating parameters.

[0024] Specifically, firstly, the target load value is determined by combining the energy supply capacity provided in the initial energy supply plan and the dynamic demand of each time period in the load forecast results, and resources are allocated to high-load periods in priority to avoid oversupply or undersupply. Secondly, through the supply and demand fluctuation balance strategy, when energy is surplus, energy storage equipment is called for energy storage first, and energy is reasonably allocated to reduce waste. When energy is insufficient, key load demand is met first, while the energy supply intensity of non-critical loads is reduced, and energy storage equipment is released to make up for the supply and demand gap. On this basis, combined with the optimal operating parameters generated by the optimization algorithm (such as compressor frequency, refrigerant flow rate and energy storage calling strategy), specific equipment operation strategies are formulated for different working conditions to ensure that the operating parameters match the real-time load demand, and optimize energy consumption and operation stability. By adopting an integrated method, the initial energy supply plan, load forecast results, supply and demand balance strategy and the weight of optimization parameters are comprehensively analyzed to generate a dynamic energy supply control strategy. In addition, during the execution process, the changes in environmental conditions and load demand are monitored in real time, and the strategy is dynamically adjusted according to the feedback results to ensure the balance of system supply and demand, efficient operation and overall stability.

[0025] As a preferred embodiment of the above, Figure 2 As shown, step S1, based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, dynamically adjust the supply intensity of new energy to obtain an initial energy supply plan, including: S11: Collect environmental data, which serves as the basis for analyzing the characteristics of new energy supply; S12: Based on the real-time collected environmental data, analyze the supply characteristics and short-term supply capacity of new energy, and calculate the load demand in combination with the current operation demand of the heat pump; S13: Calculate the available new energy input power based on the analysis results of new energy supply and demand, and generate an initial energy supply plan for heat pump operation; S14: Dynamically optimize the energy supply plan through fuzzy logic algorithm to balance the fluctuation of new energy and heat pump operation.

[0026] Specifically, first, environmental data including solar radiation intensity, wind speed, temperature, humidity, etc. are collected in real time through sensors and monitoring equipment. These data serve as the basis for the analysis of new energy supply characteristics and provide accurate input for the formulation of subsequent energy supply plans. According to the real-time collected environmental data, the supply characteristics of new energy (such as solar radiation intensity and available wind power) are analyzed to predict the supply capacity in the short term. At the same time, combined with the current operating status of the heat pump and the system demand, the load demand in different time periods is calculated to provide a basis for energy supply matching. Based on the analysis results of new energy supply and demand, the current available new energy input power of the system is calculated. By integrating the supply capacity and load demand, the initial energy supply plan of the heat pump system is generated, and key parameters such as energy supply intensity, priority areas and time allocation are clarified. Fuzzy logic algorithms are used to dynamically optimize the initial energy supply plan, and the energy supply allocation strategy is adjusted according to the volatility of new energy supply and the real-time changes in heat pump operation requirements. Fuzzy logic algorithms achieve supply and demand balance through rule reasoning (such as "when there is surplus supply and low load, energy storage is prioritized") to ensure that the energy supply plan is both efficient and stable.

[0027] As a preferred embodiment of the above, Figure 3 As shown, step 12, based on the real-time collected data, analyze the supply characteristics and short-term supply capacity of new energy, and calculate the load demand in combination with the current operation demand of the heat pump, including: S121: Collect multi-source data and operation status data related to load demand, and standardize the data as input for load demand calculation; S122: Calculate the load demand using a hierarchical forecasting model and generate a dynamic load forecasting result; S123: Divide the energy supply area into multiple sub-areas, calculate the load demand of each area respectively, and make coordinated adjustments between the areas to generate a global load demand.

[0028] Specifically, first, multi-source data related to load demand are collected in real time, including environmental data (such as temperature, humidity, solar radiation intensity, etc.), historical operation data, and current operating status data of the heat pump (such as compressor frequency, refrigerant flow). These data are standardized to ensure data consistency and provide high-quality input for subsequent load demand calculations; then, the load demand is calculated using a prediction model to generate dynamic load forecast results and provide demand change trends for future time periods; finally, based on the prediction results, the energy supply area is divided into multiple sub-areas (such as residential areas, commercial areas, and industrial areas), and the load demand of each sub-area is calculated separately, and global optimization is achieved through coordinated adjustments between regions. For example, energy supply can be appropriately reduced in some areas with lower loads, and surplus energy can be allocated to areas with higher loads, thereby ensuring the balance and efficient use of overall energy supply resources.

[0029] As a preferred embodiment of the above, Figure 4 As shown, step S122, using a hierarchical forecasting model to calculate the load demand and generate a dynamic load forecasting result, including: S1221: Analyze the real-time environmental data based on the long short-term memory network, predict the load demand in the next few hours, and obtain short-term prediction results; S1222: Analyze historical load data and seasonal fluctuation trends based on deep neural networks to obtain long-term trend forecast results; S1223: The short-term prediction results are integrated with the long-term trend prediction results through weighted calculation to generate dynamic load prediction results.

[0030] Specifically, the long short-term memory network (LSTM) model is used to dynamically analyze the environmental data (such as temperature, humidity, solar radiation intensity, etc.) collected in real time. The LSTM model can capture the short-term change patterns in time series data, and predict the change trend of load demand in the next few hours by learning the immediate impact of environmental data on load demand, and generate short-term prediction results to provide support for responding to rapidly fluctuating load demand; the deep neural network (DNN) is used to analyze historical load data, and combined with seasonal fluctuations and cyclical changes, the load demand trend in the next few days to weeks is predicted. DNN can extract complex nonlinear relationships and capture the long-term patterns in historical data to generate long-term trend prediction results, providing a basis for the system's medium- and long-term energy supply planning; the short-term prediction results are fused with the long-term trend prediction results through weighted calculation to generate the final dynamic load prediction results. The weighting coefficient can be dynamically adjusted according to actual demand. For example, when the load demand changes drastically, the weight of the short-term prediction is increased; and when the demand is relatively stable, the weight of the long-term trend is increased. The fused prediction results comprehensively consider short-term load fluctuations and long-term trend changes, providing a more accurate basis for the formulation of energy supply strategies.

[0031] As a preferred embodiment of the above, Figure 5 As shown, step S2 is to construct a load prediction model to predict the heat load demand in different time periods, and dynamically adjust the heat pump operating parameters according to the prediction results, including: S21: Calculate the target load value for each time period according to the dynamic load forecast result, and mark the high load demand period as a priority processing period; S22: Based on the target load value, dynamically adjust the heat pump operating parameters, and optimize and adjust the operating parameters by continuously monitoring the operating status of the heat pump system and real-time environmental changes.

[0032] Specifically, according to the dynamic load forecast results, the heat load demand in each future period is analyzed, and the target load value of each period is calculated. The target load value is used to guide the energy supply intensity distribution of the heat pump system in different time periods. In addition, by comparing the changing trends of load demand, the periods of high load demand are identified, and these periods are marked as priority processing periods. For example, in the period of high load demand, the system energy supply capacity is prioritized to meet the demand; while in the period of low load demand, the system energy supply output is optimized to save energy; according to the calculated target load value, the operating parameters of the heat pump are dynamically adjusted, including the compressor operating frequency, refrigerant flow rate and the calling strategy of the energy storage device, etc. By adjusting the parameters, it is ensured that the energy supply intensity matches the target load value to avoid energy waste or supply and demand imbalance caused by insufficient or excessive energy supply. At the same time, through continuous monitoring of the operating status of the heat pump system and real-time environmental conditions (such as temperature, humidity, etc.), the operating parameters are dynamically optimized and adjusted. For example, when the ambient temperature fluctuates greatly or the equipment load fluctuates violently, the operating parameters are timely optimized to maintain the efficient operation of the system.

[0033] As a preferred embodiment of the above embodiment, the optimization algorithm is a particle swarm optimization algorithm.

[0034] As a preferred embodiment of the above, the particle swarm optimization method includes: Randomly generate a particle population, each particle represents a combination of heat pump operating parameters, and set the inertia weight and learning factor; Construct objective functions based on minimizing energy consumption, maximizing load matching, and improving operational stability, and calculate particle fitness values; According to the particle fitness value, the individual optimal value of the particle and the global optimal value of the population are updated, and the heat pump operating parameters are adjusted by updating the speed and position; The global optimal solution is gradually approached in multiple iterations until the preset convergence conditions are met and the optimal operating parameters are obtained.

[0035] Specifically, first, a particle population is randomly generated, and each particle represents a combination of a set of heat pump operating parameters, such as compressor operating frequency, refrigerant flow rate and energy storage call strategy. The position and speed of the particle are initialized, and the inertia weight and learning factor are set. The inertia weight is used to control the search range, and the learning factor is used to adjust the degree of dependence of the particle on its own optimal value and the group optimal value; then, an objective function is constructed, and the optimization goals such as minimizing energy consumption, maximizing load matching and improving operation stability are comprehensively considered. The fitness value of each particle is calculated to evaluate the advantages and disadvantages of the parameter combination. In each iteration, the individual optimal value (pBest) of the particle and the global optimal value (gBest) of the population are updated according to the fitness value. The individual optimal value records the historical best solution of the particle, and the global optimal value represents the optimal solution in the current population; then, the speed and position of the particle are adjusted based on the inertia weight and learning factor. The speed update formula comprehensively considers the current speed, individual optimal value and global optimal value, and combines random factors to ensure the diversity of the search. The position update formula adjusts the position of the particle according to its new speed to further explore the optimization solution. In multiple iterations, the particle swarm continues to approach the global optimal solution. When the convergence conditions are met (such as the objective function change is less than the set threshold or the maximum number of iterations is reached), the final optimization result is output; finally, the optimal parameter combination for heat pump operation is obtained through the particle swarm optimization method, including compressor frequency, refrigerant flow and energy storage call strategy. These parameters are used to dynamically adjust the operation of the heat pump to ensure maximum energy supply efficiency, accurate matching of load demand, and stability and efficiency of system operation.

[0036] As a preferred embodiment of the above embodiment, the analytic hierarchy process is used to integrate the initial energy supply plan, load forecasting results, supply and demand fluctuation balance strategy and optimized operating parameters to generate a dynamic energy supply control strategy.

[0037] Specifically, dynamic energy supply control needs to comprehensively consider multiple complex factors, which may have mutual influence and weight differences. The AHP decomposes the problem into the target layer, the criterion layer and the factor layer by constructing a hierarchical structure model, and gradually analyzes the weight and influence of each part, so as to make the decision process clearer and more orderly. In addition, the AHP can dynamically quantify the importance of each part. For example, when the load demand fluctuates greatly, the weight of the load forecast result may be higher, while when the energy supply conditions fluctuate, the supply and demand balance strategy may dominate. By constructing a weight matrix and making consistency judgments, the AHP can flexibly adjust the weights according to the actual working conditions, so as to adapt to the strategy generation requirements under different conditions. At the same time, the AHP provides a structured and transparent decision-making process to ensure that each step from problem decomposition to weight calculation and comprehensive sorting is logical and explainable. More importantly, the AHP supports multi-objective integration, and can balance multiple objectives such as minimizing energy consumption, maximizing load matching and improving operation stability in energy management. Through weight calculation and comprehensive scoring, the dynamic energy supply control strategy finally generated can scientifically integrate the advantages of each part and meet the system's efficient operation requirements.

[0038] Embodiment 2 Based on the same inventive concept as the new energy heat pump integrated control method in the aforementioned embodiment, the present invention further provides a new energy heat pump integrated control system, the system comprising: The initial plan acquisition module dynamically adjusts the supply intensity of new energy based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, to obtain the initial energy supply plan; The prediction model building module builds a load prediction model to predict the heat load demand in different periods of time and dynamically adjusts the heat pump operating parameters based on the prediction results; The supply and demand fluctuation balancing module balances the supply and demand fluctuations under the conditions of energy surplus and shortage according to the initial functional plan and the changing trend of heat load demand; Dynamic analysis and optimization module, which dynamically analyzes the real-time collected environmental data and equipment operating status based on the optimization algorithm to optimize the operating parameters; The energy supply strategy generation module generates a dynamic energy supply control strategy based on the initial energy supply plan, load forecast results, supply and demand fluctuation balance strategy and optimized operating parameters.

[0039] The above-mentioned control system in the present invention can effectively implement the integrated control method of the new energy heat pump, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0040] As a preferred embodiment of the above, the initial solution acquisition module includes: Environmental data collection unit, which collects environmental data as the basis for analyzing the characteristics of new energy supply; The supply capacity analysis unit analyzes the supply characteristics and short-term supply capacity of new energy based on real-time collected environmental data, and calculates the load demand in combination with the current operation demand of the heat pump; An input power calculation unit calculates the available input power of new energy according to the analysis results of new energy supply and demand, and generates an initial energy supply plan for the operation of the heat pump; The fuzzy algorithm optimization unit dynamically optimizes the energy supply plan through fuzzy logic algorithm to balance the fluctuation of new energy and the operation of heat pumps.

[0041] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0042] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A new energy heat pump integrated control method, characterized in that: include: Based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, the supply intensity of new energy is dynamically adjusted to obtain the initial energy supply plan; Build a load forecasting model to predict the heat load demand in different periods of time, and dynamically adjust the heat pump operating parameters based on the forecast results; According to the initial functional scheme and the changing trend of heat load demand, balance the fluctuation of supply and demand under the conditions of energy surplus and shortage; Dynamically analyze the real-time collected environmental data and equipment operating status based on optimization algorithms to optimize operating parameters; A dynamic energy supply control strategy is generated based on the initial energy supply plan, load forecasting results, supply and demand fluctuation balancing strategy and optimized operating parameters.

2. The new energy heat pump integrated control method according to claim 1 is characterized in that: Based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, the supply intensity of new energy is dynamically adjusted to obtain the initial energy supply plan, including: Collecting environmental data as a basis for analyzing the characteristics of new energy supply; Based on the environmental data collected in real time, analyzing the supply characteristics and short-term supply capacity of new energy, and calculating the load demand in combination with the current operation demand of the heat pump; Based on the analysis results of new energy supply and demand, the available new energy input power is calculated and the initial energy supply plan for heat pump operation is generated; The energy supply scheme is dynamically optimized through fuzzy logic algorithms to balance the fluctuation of new energy and the operation of heat pumps.

3. The new energy heat pump integrated control method according to claim 2 is characterized in that: Based on real-time collected data, the supply characteristics and short-term supply capacity of new energy are analyzed, and the load demand is calculated in combination with the current operation demand of the heat pump, including: Collecting multi-source data and operating status data related to load demand and standardizing the data as input for load demand calculation; Use a hierarchical forecasting model to calculate load demand and generate dynamic load forecast results; The energy supply area is divided into multiple sub-areas, the load demand of each area is calculated separately, and coordinated adjustments are made between areas to generate the global load demand.

4. The new energy heat pump integrated control method according to claim 3 is characterized in that: A hierarchical forecasting model is used to calculate load demand and generate dynamic load forecast results, including: Analyze real-time environmental data based on long short-term memory networks to predict load demand in the next few hours and obtain short-term prediction results; Analyze historical load data and seasonal fluctuation trends based on deep neural networks to obtain long-term trend forecast results; The short-term prediction result and the long-term trend prediction result are fused through weighted calculation to generate the dynamic load prediction result.

5. The new energy heat pump integrated control method according to claim 4 is characterized in that: Build a load forecasting model to predict the heat load demand in different periods of time, and dynamically adjust the heat pump operating parameters based on the forecast results, including: According to the dynamic load forecasting results, the target load value of each time period is calculated, and the high load demand time period is marked as a priority processing time period; Based on the target load value, the heat pump operating parameters are dynamically adjusted, and the operating parameters are optimized and adjusted through continuous monitoring of the heat pump system operating status and real-time environmental changes.

6. The new energy heat pump integrated control method according to claim 1, characterized in that: The optimization algorithm is a particle swarm optimization algorithm.

7. The new energy heat pump integrated control method according to claim 6, characterized in that: The particle swarm optimization method comprises: Randomly generate a particle population, each particle represents a combination of heat pump operating parameters, and set the inertia weight and learning factor; Construct objective functions based on minimizing energy consumption, maximizing load matching, and improving operational stability, and calculate particle fitness values; According to the fitness value of the particle, the individual optimal value of the particle and the global optimal value of the population are updated, and the operating parameters of the heat pump are adjusted by updating the speed and position; The global optimal solution is gradually approached in multiple iterations until the preset convergence conditions are met and the optimal operating parameters are obtained.

8. The new energy heat pump integrated control method according to claim 1, characterized in that: The initial energy supply plan, load forecasting results, supply and demand fluctuation balance strategy and optimized operating parameters are integrated by using the hierarchical analysis method to generate the dynamic energy supply control strategy.

9. A new energy heat pump integrated control system, characterized in that: The system comprises: The initial plan acquisition module dynamically adjusts the supply intensity of new energy based on the real-time collection results of new energy, combined with environmental conditions and supply characteristics, to obtain the initial energy supply plan; The prediction model building module builds a load prediction model to predict the heat load demand in different periods of time and dynamically adjusts the heat pump operating parameters based on the prediction results; A supply and demand fluctuation balancing module, which balances the supply and demand fluctuations under the conditions of energy surplus and shortage according to the initial functional scheme and the changing trend of the heat load demand; Dynamic analysis and optimization module, which dynamically analyzes the real-time collected environmental data and equipment operating status based on the optimization algorithm to optimize the operating parameters; The energy supply strategy generation module generates a dynamic energy supply control strategy based on the initial energy supply plan, load forecasting results, supply and demand fluctuation balancing strategy and optimized operating parameters.

10. The new energy heat pump integrated control system according to claim 9, characterized in that: The initial solution acquisition module includes: An environmental data collection unit collects environmental data, which is used as a basis for analyzing the characteristics of new energy supply; A supply capacity analysis unit, based on the environmental data collected in real time, analyzes the supply characteristics and short-term supply capacity of new energy, and calculates the load demand in combination with the current operation demand of the heat pump; An input power calculation unit calculates the available input power of new energy according to the analysis results of new energy supply and demand, and generates an initial energy supply plan for the operation of the heat pump; The fuzzy algorithm optimization unit dynamically optimizes the energy supply plan through fuzzy logic algorithm to balance the fluctuation of new energy and the operation of heat pumps.

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